- DSA - Home
- DSA - Overview
- DSA - Environment Setup
- DSA - Algorithms Basics
- DSA - Asymptotic Analysis
- Data Structures
- DSA - Data Structure Basics
- DSA - Data Structures and Types
- DSA - Array Data Structure
- DSA - Skip List Data Structure
- Linked Lists
- DSA - Linked List Data Structure
- DSA - Doubly Linked List Data Structure
- DSA - Circular Linked List Data Structure
- Stack & Queue
- DSA - Stack Data Structure
- DSA - Expression Parsing
- DSA - Queue Data Structure
- DSA - Circular Queue Data Structure
- DSA - Priority Queue Data Structure
- DSA - Deque Data Structure
- Searching Algorithms
- DSA - Searching Algorithms
- DSA - Linear Search Algorithm
- DSA - Binary Search Algorithm
- DSA - Interpolation Search
- DSA - Jump Search Algorithm
- DSA - Exponential Search
- DSA - Fibonacci Search
- DSA - Sublist Search
- DSA - Hash Table
- Sorting Algorithms
- DSA - Sorting Algorithms
- DSA - Bubble Sort Algorithm
- DSA - Insertion Sort Algorithm
- DSA - Selection Sort Algorithm
- DSA - Merge Sort Algorithm
- DSA - Shell Sort Algorithm
- DSA - Heap Sort Algorithm
- DSA - Bucket Sort Algorithm
- DSA - Counting Sort Algorithm
- DSA - Radix Sort Algorithm
- DSA - Quick Sort Algorithm
- Matrices Data Structure
- DSA - Matrices Data Structure
- DSA - Lup Decomposition In Matrices
- DSA - Lu Decomposition In Matrices
- Graph Data Structure
- DSA - Graph Data Structure
- DSA - Depth First Traversal
- DSA - Breadth First Traversal
- DSA - Spanning Tree
- DSA - Topological Sorting
- DSA - Strongly Connected Components
- DSA - Biconnected Components
- DSA - Augmenting Path
- DSA - Network Flow Problems
- DSA - Flow Networks In Data Structures
- DSA - Edmonds Blossom Algorithm
- DSA - Maxflow Mincut Theorem
- Tree Data Structure
- DSA - Tree Data Structure
- DSA - Tree Traversal
- DSA - Binary Search Tree
- DSA - AVL Tree
- DSA - Red Black Trees
- DSA - B Trees
- DSA - B+ Trees
- DSA - Splay Trees
- DSA - Range Queries
- DSA - Segment Trees
- DSA - Fenwick Tree
- DSA - Fusion Tree
- DSA - Hashed Array Tree
- DSA - K-Ary Tree
- DSA - Kd Trees
- DSA - Priority Search Tree Data Structure
- Recursion
- DSA - Recursion Algorithms
- DSA - Tower of Hanoi Using Recursion
- DSA - Fibonacci Series Using Recursion
- Divide and Conquer
- DSA - Divide and Conquer
- DSA - Max-Min Problem
- DSA - Strassen's Matrix Multiplication
- DSA - Karatsuba Algorithm
- Greedy Algorithms
- DSA - Greedy Algorithms
- DSA - Travelling Salesman Problem (Greedy Approach)
- DSA - Prim's Minimal Spanning Tree
- DSA - Kruskal's Minimal Spanning Tree
- DSA - Dijkstra's Shortest Path Algorithm
- DSA - Map Colouring Algorithm
- DSA - Fractional Knapsack Problem
- DSA - Job Sequencing with Deadline
- DSA - Optimal Merge Pattern Algorithm
- Dynamic Programming
- DSA - Dynamic Programming
- DSA - Matrix Chain Multiplication
- DSA - Floyd Warshall Algorithm
- DSA - 0-1 Knapsack Problem
- DSA - Longest Common Sub-sequence Algorithm
- DSA - Travelling Salesman Problem (Dynamic Approach)
- Hashing
- DSA - Hashing Data Structure
- DSA - Collision In Hashing
- Disjoint Set
- DSA - Disjoint Set
- DSA - Path Compression And Union By Rank
- Heap
- DSA - Heap Data Structure
- DSA - Binary Heap
- DSA - Binomial Heap
- DSA - Fibonacci Heap
- Tries Data Structure
- DSA - Tries
- DSA - Standard Tries
- DSA - Compressed Tries
- DSA - Suffix Tries
- Treaps
- DSA - Treaps Data Structure
- Bit Mask
- DSA - Bit Mask In Data Structures
- Bloom Filter
- DSA - Bloom Filter Data Structure
- Approximation Algorithms
- DSA - Approximation Algorithms
- DSA - Vertex Cover Algorithm
- DSA - Set Cover Problem
- DSA - Travelling Salesman Problem (Approximation Approach)
- Randomized Algorithms
- DSA - Randomized Algorithms
- DSA - Randomized Quick Sort Algorithm
- DSA - Karger’s Minimum Cut Algorithm
- DSA - Fisher-Yates Shuffle Algorithm
- Miscellaneous
- DSA - Infix to Postfix
- DSA - Bellmon Ford Shortest Path
- DSA - Maximum Bipartite Matching
- DSA Useful Resources
- DSA - Questions and Answers
- DSA - Selection Sort Interview Questions
- DSA - Merge Sort Interview Questions
- DSA - Insertion Sort Interview Questions
- DSA - Heap Sort Interview Questions
- DSA - Bubble Sort Interview Questions
- DSA - Bucket Sort Interview Questions
- DSA - Radix Sort Interview Questions
- DSA - Cycle Sort Interview Questions
- DSA - Quick Guide
- DSA - Useful Resources
- DSA - Discussion
Data Structures Algorithms Online Quiz
Following quiz provides Multiple Choice Questions (MCQs) related to Data Structures Algorithms. You will have to read all the given answers and click over the correct answer. If you are not sure about the answer then you can check the answer using Show Answer button. You can use Next Quiz button to check new set of questions in the quiz.
Q 1 - A procedure that calls itself is called
Answer : C
Explanation
In recursion, a procedure calls itself, either directly or by calling a procedure which in turn calls it.
Q 2 - Maximum number of nodes in a binary tree with height k, where root is height 0, is
Answer : B
Explanation
If the root node is at height 0, then a binary tree can have at max 2k+1 − 1 nodes.
For example: a binary tree of height 1, can have maximum 21+1 − 1 = 3 nodes.
r --------- 0 / \ L R --------- 1
Q 3 - Minimum number of moves required to solve a Tower of Hanoi puzzle is
Answer : C
Explanation
Minimum number of moves required to solve a Tower of Hanoi puzzle is 2n - 1. Where n is the number of disks. If the number of disks is 3, then minimum number of moves required are 23 - 1 = 7
Q 4 - Maximum degree of any vertex in a simple graph of vertices n is
Answer : D
Explanation
In a simple graph, a vertex can have edge to maximum n - 1 vertices.
Q 5 - What about recursion is true in comparison with iteration?
A - very expensive in terms of memory.
C - every recursive program can be written with iteration too.
Answer : D
Explanation
Recursion is just an other way to write the same program code. But calling a function again and again makes it expensive in terms of memory, CPU cycles and delivers less performance.
Q 6 - The number of binary trees with 3 nodes which when traversed in post order gives the sequence A,B,C is ?
Answer : C
Explanation
Five binary trees (of 3 nodes) are possible.
Q 7 - The Θ notation in asymptotic evaluation represents −
Answer : A
Explanation
Θ represents average case. Ο represents worst case and Ω represents base case.
Q 8 - All possible spanning trees of graph G
A - have same number of edges and vertices.
B - have same number of edges and but not vertices.
Answer : A
Explanation
All possible spanning trees of graph G, have same number of edges and vertices.
Q 9 - Access time of a binary search tree may go worse in terms of time complexity upto
Answer : C
Explanation
At maximum, BST may need to search all n values in the tree in order to access an element, hence, Ο(n).
Q 10 - Aposterior analysis are more accurate than apriori analysis because −
A - it contains the real data.
B - it assumes all other factors to be dynamic.
Answer : B
Explanation
In this analysis, actual statistics like running time and space required, are collected.